You May Also Enjoy
Featured paper: TopoFlow: topography-aware pollutant Flow learning for high-resolution air quality prediction
Published:
This month's Climate AI Nordics featured paper is "TopoFlow: topography-aware pollutant Flow learning for high-resolution air quality prediction". This work introduces a physics-guided transformer that incorporates wind and topography directly into the model to improve air quality forecasting.
Featured member, October 2026: Mélisande Teng
Published:
Mélisande Teng is a postdoctoral researcher at the University of Copenhagen and the Pioneer Centre for AI. She develops machine learning methods for biodiversity monitoring, in particular species distribution modeling, forest monitoring with remote sensing and bioacoustics.
AtmoSTEM: From high-resolution emission mapping to environmental intelligence
Event date:
Webinar with Anastasia Kakouri, University of Aegean. The spatial and temporal representation of anthropogenic emissions is essential for atmospheric modelling, yet its influence on the predictive performance of data-driven air quality models remains insufficiently assessed. This study introduces AtmoSTEM, a modular European framework integrating high-resolution emission modelling with machine learning-based concentration prediction. AtmoSTEM combines CAMS-REG emission inventories, sector-specific geospatial proxies, and CAMS-TEMPO profiles to generate spatially and temporally resolved anthropogenic emissions. The initial application covers daily PM2.5 and PM10 emissions at 1 km spatial resolution across Europe for 2015–2024. The resulting emissions are integrated with meteorological, satellite-derived, atmospheric composition, and geospatial information to support machine learning-based predictions of ground-level pollutant concentrations. An Extreme Gradient Boosting (XGBoost) approach is employed to investigate the relationships between anthropogenic emissions, environmental conditions, and observed air pollution levels, with particular emphasis on the contribution of high-resolution emission information to predictive performance. Model predictions are evaluated against independent ground-based observations from air quality monitoring stations across Europe. The study aims to assess the potential of integrating high-resolution spatiotemporal emissions with machine learning for air quality prediction, providing a basis for more spatially detailed population exposure assessments and epidemiological research.
2027 Nordic Workshop on AI for Climate
Event date:
The 2027 Nordic Workshop on AI for Climate will gather researchers from the Nordics. This one-day, in-person workshop, will take place in Oslo, April 22n 2027. The workshop will feature a mix of keynotes, oral presentations, and posters around the topics of AI for tackling climate change, including AI for biodiversity and the green transition. The workshop will be a meeting point for a wide range of researchers from (primarily) around the Nordic countries.